Association of metabolic syndrome components with knee osteoarthritis: a Mendelian randomization study_Chinese Journal of Evidence-Based Medicine

Authors：

WANG Huida , ZHANG Zhi , WANG Hanyu ,  MA Xing

Department of Orthopedics, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P. R. China;

Corresponding?author：

MA Xing, Email: maxing2826@mail.xjtu.edu.cn

Keywords：

Mendelian randomization; Knee osteoarthritis; Metabolic syndrome

DOI：

10.7507/1672-2531.202307112

Video：

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Objective To investigate the causal association between metabolic syndrome (MetS) components and osteoarthritis of the knee (KOA) by using Mendelian randomization analysis. Methods The genome-wide association study database (GWAS) was mined, in which the exposure factors were MetS components, namely waist circumference (WC) level, triglyceride (TG) level, high-density lipoprotein cholesterol (HDL-C) level, hypertension (HBP), and type 2 diabetes (T2DM), and the outcome factor was KOA. Mendelian randomization analysis was performed using regression models of inverse-variance weighted (IVW), MR-Egger, Simple Mode, Weighted Median, and Weighted Mode methods. Results IVW showed a causal relationship between WC level and KOA with a positive correlation (OR=3.088, 95%CI 2.574 to 3.704, P<0.01), and HDL-C level had a causal relationship with KOA with a negative correlation (OR=0.877, 95%CI 0.779 to 0.989, P<0.05). IVW did not show a causal relationship between TG levels, HBP, and T2DM with KOA (P>0.05). The results of the ME-Egger intercept test were not multiplicative (P>0.05), indicating that Mendelian randomization was a valid method for causal inference in this study. Conclusion Central obesity and low HDL-C disorder are independent risk factors for KOA. The causal relationship between TG level, HBP, and T2DM with KOA is still uncertain.

Citation： WANG Huida, ZHANG Zhi, WANG Hanyu, MA Xing. Association of metabolic syndrome components with knee osteoarthritis: a Mendelian randomization study. Chinese Journal of Evidence-Based Medicine, 2023, 23(11): 1241-1246. doi: 10.7507/1672-2531.202307112 Copy

1.	北京醫學會骨科學分會關節外科學組. 老年骨關節炎及骨質疏松癥診斷與治療社區管理專家共識(2023版). 協和醫學雜志, 2023, 14(3): 484-493.
2.	張瑩瑩, 李旭東, 楊佳娟, 等. 中國40歲及以上人群骨關節炎患病率的Meta分析. 中國循證醫學雜志, 2021, 21(4): 407-414.
3.	Cieza A, Causey K, Kamenov K, et al. Global estimates of the need for rehabilitation based on the global burden of disease study 2019: a systematic analysis for the global burden of disease study 2019. Lancet, 2021, 396(10267): 2006-2017.
4.	Safiri S, Kolahi AA, Smith E, et al. Global, regional and national burden of osteoarthritis 1990-2017: a systematic analysis of the global burden of disease study 2017. Ann Rheum Dis, 2020, 79(6): 819-828.
5.	Abramoff B, Caldera FE. Osteoarthritis: pathology, diagnosis, and treatment options. Med Clin North Am, 2020, 104(2): 293-311.
6.	許成燕, 陳軍香, 王教明, 等. 中國人群膝骨關節炎危險因素的Meta分析. 中國循證醫學雜志, 2021, 21(7): 772-778.
7.	中華醫學會運動醫療分會, 中國醫師協會骨科醫師分會運動醫學學組, 中國醫師協會骨科醫師分會關節鏡學組. 骨關節炎臨床藥物治療專家共識. 中國醫學前沿雜志(電子版), 2021, 13(7): 32-43.
8.	Xie Y, Zhou W, Zhong Z, et al. Metabolic syndrome, hypertension, and hyperglycemia were positively associated with knee osteoarthritis, while dyslipidemia showed no association with knee osteoarthritis. Clin Rheumatol, 2021, 40(2): 711-724.
9.	Fahed G, Aoun L, Bou Zerdan M, et al. Metabolic syndrome: updates on pathophysiology and management in 2021. Int J Mol Sci, 2022, 23(2): 786.
10.	中華醫學會老年醫學分會老年內分泌代謝疾病學組中國老年代謝綜合征藥物治療專家共識(2022)編寫組. 中國老年人代謝綜合征藥物治療專家共識(2022). 中華老年醫學雜志, 2022, 41(9): 1011-1027.
11.	李波. 骨關節炎與代謝綜合征相關機制的系統綜述. 嶺南現代臨床外科, 2021, 21(6): 679-683.
12.	Batushansky A, Zhu S, Komaravolu RK, et al. Fundamentals of OA. An initiative of osteoarthritis and cartilage. Obesity and metabolic factors in OA. Osteoarthritis Cartilage, 2022, 30(4): 501-515.
13.	于天琦, 徐文濤, 蘇雅娜, 等. 孟德爾隨機化研究基本原理、方法和局限性. 中國循證醫學雜志, 2021, 21(10): 1227-1234.
14.	CSCO生物統計學專家委員會RWS方法學組. 孟德爾隨機化模型及其規范化應用的統計學共識. 中國衛生統計, 2021, 38(3): 471-475,480.
15.	Pingault JB, O'Reilly PF, Schoeler T, et al. Using genetic data to strengthen causal inference in observational research. Nat Rev Genet, 2018, 19(9): 566-580.
16.	Elsworth BL, Lyon M S, Alexander T, et al. The mrc ieu opengwas data infrastructure. Cold Spring Harbor Laboratory, 2020.
17.	Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife, 2018, 7: e34408.
18.	Lyon MS, Andrews SJ, Elsworth B, et al. The variant call format provides efficient and robust storage of GWAS summary statistics. Genome Biol, 2021, 22(1): 32.
19.	Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 2015, 12(3): e1001779.
20.	Xue A, Wu Y, Zhu Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun, 2018, 9(1): 2941.
21.	Richardson TG, Sanderson E, Palmer TM, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable Mendelian randomisation analysis. PLoS Med, 2020, 17(3): e1003062.
22.	Zengini E, Hatzikotoulas K, Tachmazidou I, et al. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Genet, 2018, 50(4): 549-558.
23.	Liu SY, Zhu WT, Chen BW, et al. Bidirectional association between metabolic syndrome and osteoarthritis: a meta-analysis of observational studies. Diabetol Metab Syndr, 2020, 12: 38.
24.	Sanchez-Santos MT, Judge A, Gulati M, et al. Association of metabolic syndrome with knee and hand osteoarthritis: a community-based study of women. Semin Arthritis Rheum, 2019, 48(5): 791-798.
25.	Yasuda E, Nakamura R, Matsugi R, et al. Association between the severity of symptomatic knee osteoarthritis and cumulative metabolic factors. Aging Clin Exp Res, 2018, 30(5): 481-488.
26.	Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res, 2023, 4: 186.
27.	Kumavat R, Kumar V, Malhotra R, et al. Biomarkers of joint damage in osteoarthritis: current status and future directions. Mediators Inflamm, 2021, 2021: 5574582.
28.	Andriacchi TP, Griffin TM, Loeser RF, et al. Bridging disciplines as a pathway to finding new solutions for osteoarthritis a collaborative program presented at the 2019 orthopaedic research society and the osteoarthritis research society international. Osteoarthr Cartil Open, 2020, 2(1): 100026.
29.	Nukala S, Puvvada SR, Luvsannyam E, et al. Hyperlipidemia and statin use on the progression of osteoarthritis: a systematic review. Cureus, 2021, 13(6): e15999.
30.	Schwager JL, Nevitt MC, Torner J, et al. Association of serum low-density lipoprotein, high-density lipoprotein, and total cholesterol with development of knee osteoarthritis. Arthritis Care Res (Hoboken), 2022, 74(2): 274-280.
31.	Zhang K, Ji Y, Dai H, et al. High-density lipoprotein cholesterol and apolipoprotein a1 in synovial fluid: potential predictors of disease severity of primary knee osteoarthritis. Cartilage, 2021, 13(1_suppl): 1465S-1473S.
32.	Meng H, Jiang L, Song Z, et al. Causal associations of circulating lipids with osteoarthritis: a bidirectional Mendelian randomization study. Nutrients, 2022, 14(7): 1327.
33.	Wang Z, Liu M, Zhou Y, et al. Effect of blood lipids and lipid-lowering therapies on osteoarthritis risk: a Mendelian randomization study. Front Med (Lausanne), 2022, 9: 990569.
34.	Ching K, Houard X, Berenbaum F, et al. Hypertension meets osteoarthritis - revisiting the vascular aetiology hypothesis. Nat Rev Rheumatol, 2021, 17(9): 533-549.
35.	Niu J, Clancy M, Aliabadi P, et al. Metabolic syndrome, its components, and knee osteoarthritis: the framingham osteoarthritis study. Arthritis Rheumatol, 2017, 69(6): 1194-1203.
36.	Funck-Brentano T, Nethander M, Movérare-Skrtic S, et al. Causal factors for knee, hip, and hand osteoarthritis: a Mendelian randomization study in the UK biobank. Arthritis Rheumatol, 2019, 71(10): 1634-1641.
37.	Hindy G, ?kesson KE, Melander O, et al. Cardiometabolic polygenic risk scores and osteoarthritis outcomes: a Mendelian randomization study using data from the malm? diet and cancer study and the UK biobank. Arthritis Rheumatol, 2019, 71(6): 925-934.
38.	Centers For Disease C, Prevention. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation-United States, 2010-2012. MMWR Morb Mortal Wkly Rep, 2013, 62(44): 869-873.
39.	Louati K, Vidal C, Berenbaum F, et al. Association between diabetes mellitus and osteoarthritis: systematic literature review and meta-analysis. RMD Open, 2015, 1(1): e000077.
40.	Alenazi AM, Alhowimel AS, Alshehri MM, et al. Osteoarthritis and diabetes: where are we and where should we go. Diagnostics (Basel), 2023, 13(8): 1386.
41.	Veronese N, Cooper C, Reginster JY, et al. Type 2 diabetes mellitus and osteoarthritis. Semin Arthritis Rheum, 2019, 49(1): 9-19.

1. 北京醫學會骨科學分會關節外科學組. 老年骨關節炎及骨質疏松癥診斷與治療社區管理專家共識(2023版). 協和醫學雜志, 2023, 14(3): 484-493.
2. 張瑩瑩, 李旭東, 楊佳娟, 等. 中國40歲及以上人群骨關節炎患病率的Meta分析. 中國循證醫學雜志, 2021, 21(4): 407-414.
3. Cieza A, Causey K, Kamenov K, et al. Global estimates of the need for rehabilitation based on the global burden of disease study 2019: a systematic analysis for the global burden of disease study 2019. Lancet, 2021, 396(10267): 2006-2017.
4. Safiri S, Kolahi AA, Smith E, et al. Global, regional and national burden of osteoarthritis 1990-2017: a systematic analysis of the global burden of disease study 2017. Ann Rheum Dis, 2020, 79(6): 819-828.
5. Abramoff B, Caldera FE. Osteoarthritis: pathology, diagnosis, and treatment options. Med Clin North Am, 2020, 104(2): 293-311.
6. 許成燕, 陳軍香, 王教明, 等. 中國人群膝骨關節炎危險因素的Meta分析. 中國循證醫學雜志, 2021, 21(7): 772-778.
7. 中華醫學會運動醫療分會, 中國醫師協會骨科醫師分會運動醫學學組, 中國醫師協會骨科醫師分會關節鏡學組. 骨關節炎臨床藥物治療專家共識. 中國醫學前沿雜志(電子版), 2021, 13(7): 32-43.
8. Xie Y, Zhou W, Zhong Z, et al. Metabolic syndrome, hypertension, and hyperglycemia were positively associated with knee osteoarthritis, while dyslipidemia showed no association with knee osteoarthritis. Clin Rheumatol, 2021, 40(2): 711-724.
9. Fahed G, Aoun L, Bou Zerdan M, et al. Metabolic syndrome: updates on pathophysiology and management in 2021. Int J Mol Sci, 2022, 23(2): 786.
10. 中華醫學會老年醫學分會老年內分泌代謝疾病學組中國老年代謝綜合征藥物治療專家共識(2022)編寫組. 中國老年人代謝綜合征藥物治療專家共識(2022). 中華老年醫學雜志, 2022, 41(9): 1011-1027.
11. 李波. 骨關節炎與代謝綜合征相關機制的系統綜述. 嶺南現代臨床外科, 2021, 21(6): 679-683.
12. Batushansky A, Zhu S, Komaravolu RK, et al. Fundamentals of OA. An initiative of osteoarthritis and cartilage. Obesity and metabolic factors in OA. Osteoarthritis Cartilage, 2022, 30(4): 501-515.
13. 于天琦, 徐文濤, 蘇雅娜, 等. 孟德爾隨機化研究基本原理、方法和局限性. 中國循證醫學雜志, 2021, 21(10): 1227-1234.
14. CSCO生物統計學專家委員會RWS方法學組. 孟德爾隨機化模型及其規范化應用的統計學共識. 中國衛生統計, 2021, 38(3): 471-475,480.
15. Pingault JB, O'Reilly PF, Schoeler T, et al. Using genetic data to strengthen causal inference in observational research. Nat Rev Genet, 2018, 19(9): 566-580.
16. Elsworth BL, Lyon M S, Alexander T, et al. The mrc ieu opengwas data infrastructure. Cold Spring Harbor Laboratory, 2020.
17. Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife, 2018, 7: e34408.
18. Lyon MS, Andrews SJ, Elsworth B, et al. The variant call format provides efficient and robust storage of GWAS summary statistics. Genome Biol, 2021, 22(1): 32.
19. Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 2015, 12(3): e1001779.
20. Xue A, Wu Y, Zhu Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun, 2018, 9(1): 2941.
21. Richardson TG, Sanderson E, Palmer TM, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable Mendelian randomisation analysis. PLoS Med, 2020, 17(3): e1003062.
22. Zengini E, Hatzikotoulas K, Tachmazidou I, et al. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Genet, 2018, 50(4): 549-558.
23. Liu SY, Zhu WT, Chen BW, et al. Bidirectional association between metabolic syndrome and osteoarthritis: a meta-analysis of observational studies. Diabetol Metab Syndr, 2020, 12: 38.
24. Sanchez-Santos MT, Judge A, Gulati M, et al. Association of metabolic syndrome with knee and hand osteoarthritis: a community-based study of women. Semin Arthritis Rheum, 2019, 48(5): 791-798.
25. Yasuda E, Nakamura R, Matsugi R, et al. Association between the severity of symptomatic knee osteoarthritis and cumulative metabolic factors. Aging Clin Exp Res, 2018, 30(5): 481-488.
26. Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res, 2023, 4: 186.
27. Kumavat R, Kumar V, Malhotra R, et al. Biomarkers of joint damage in osteoarthritis: current status and future directions. Mediators Inflamm, 2021, 2021: 5574582.
28. Andriacchi TP, Griffin TM, Loeser RF, et al. Bridging disciplines as a pathway to finding new solutions for osteoarthritis a collaborative program presented at the 2019 orthopaedic research society and the osteoarthritis research society international. Osteoarthr Cartil Open, 2020, 2(1): 100026.
29. Nukala S, Puvvada SR, Luvsannyam E, et al. Hyperlipidemia and statin use on the progression of osteoarthritis: a systematic review. Cureus, 2021, 13(6): e15999.
30. Schwager JL, Nevitt MC, Torner J, et al. Association of serum low-density lipoprotein, high-density lipoprotein, and total cholesterol with development of knee osteoarthritis. Arthritis Care Res (Hoboken), 2022, 74(2): 274-280.
31. Zhang K, Ji Y, Dai H, et al. High-density lipoprotein cholesterol and apolipoprotein a1 in synovial fluid: potential predictors of disease severity of primary knee osteoarthritis. Cartilage, 2021, 13(1_suppl): 1465S-1473S.
32. Meng H, Jiang L, Song Z, et al. Causal associations of circulating lipids with osteoarthritis: a bidirectional Mendelian randomization study. Nutrients, 2022, 14(7): 1327.
33. Wang Z, Liu M, Zhou Y, et al. Effect of blood lipids and lipid-lowering therapies on osteoarthritis risk: a Mendelian randomization study. Front Med (Lausanne), 2022, 9: 990569.
34. Ching K, Houard X, Berenbaum F, et al. Hypertension meets osteoarthritis - revisiting the vascular aetiology hypothesis. Nat Rev Rheumatol, 2021, 17(9): 533-549.
35. Niu J, Clancy M, Aliabadi P, et al. Metabolic syndrome, its components, and knee osteoarthritis: the framingham osteoarthritis study. Arthritis Rheumatol, 2017, 69(6): 1194-1203.
36. Funck-Brentano T, Nethander M, Movérare-Skrtic S, et al. Causal factors for knee, hip, and hand osteoarthritis: a Mendelian randomization study in the UK biobank. Arthritis Rheumatol, 2019, 71(10): 1634-1641.
37. Hindy G, ?kesson KE, Melander O, et al. Cardiometabolic polygenic risk scores and osteoarthritis outcomes: a Mendelian randomization study using data from the malm? diet and cancer study and the UK biobank. Arthritis Rheumatol, 2019, 71(6): 925-934.
38. Centers For Disease C, Prevention. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation-United States, 2010-2012. MMWR Morb Mortal Wkly Rep, 2013, 62(44): 869-873.
39. Louati K, Vidal C, Berenbaum F, et al. Association between diabetes mellitus and osteoarthritis: systematic literature review and meta-analysis. RMD Open, 2015, 1(1): e000077.
40. Alenazi AM, Alhowimel AS, Alshehri MM, et al. Osteoarthritis and diabetes: where are we and where should we go. Diagnostics (Basel), 2023, 13(8): 1386.
41. Veronese N, Cooper C, Reginster JY, et al. Type 2 diabetes mellitus and osteoarthritis. Semin Arthritis Rheum, 2019, 49(1): 9-19.

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